This document explores qualitative indicators from an ActivityInfo database that is monitoring Ecuador.
| Indicator count totals | |||||
|---|---|---|---|---|---|
| Nov 2013 to May 2019 | |||||
| Date | Quantity | Select | Single-line text | Multi-line text | % of total data collected |
| Nov 2013 | 141,442 | 30,531 | 0 | 6,309 | 3.54% |
| June 2015 | 1,887,857 | 745,841 | 85,863 | 57,128 | 2.06% |
| Sept 2016 | 3,380,991 | 1,296,548 | 191,640 | 116,184 | 2.33% |
| May 2017 | 4,932,977 | 1,809,419 | 265,196 | 168,599 | 2.35% |
| May 2019 | 12,174,327 | 7,595,829 | 2,683,945 | 915,948 | 3.92% |
From the perspective of ActivityInfo, it shows a clear need for new tools to support analysis of qualitative data as the absolute volume of qualitative data has increased by a factor of 150, and almost doubled as a relative share of all data collected.
This section gives some ideas how the raw data looks like. The data has been extracted from ActivityInfo by using the ActivityInfo API and pre-processed to make it ready for the analysis. The most of data extraction and cleaning are done beforehand (please see R/ folder in the repository especially take a close look at etl.R and etl-methods.R files). If you want to download the raw data, you must have an access for it, that can be done by sourcing the etl.R file.
Click at the button below to glimpse at the raw data and simple explanations of the columns:
## databaseId databaseName folderId folderName formId
## 1 d0000010297 ECUADOR_MONITOREO f0000021276 Objectivo_1.1 a1424455933
## 2 d0000010297 ECUADOR_MONITOREO f0000021276 Objectivo_1.1 a1424455933
## 3 d0000010297 ECUADOR_MONITOREO f0000021276 Objectivo_1.1 a1424455933
## 4 d0000010297 ECUADOR_MONITOREO f0000021276 Objectivo_1.1 a1424455933
## 5 ... ... ... ... ...
## formName subFormId subFormName recordId Month
## 1 Salud cjs13y74y2 Monthly Sub-Form s0462106109-2019-02 2019-02
## 2 Salud cjs13y74y2 Monthly Sub-Form s1724792277-2019-02 2019-02
## 3 Salud cjs13y74y2 Monthly Sub-Form s0252135214-2019-02 2019-02
## 4 Salud cjs13y74y2 Monthly Sub-Form s1709156312-2019-02 2019-02
## 5 ... ... ... ... ...
## code question
## 1 Act_4_Ind_1 # de kits de SSR en la emergencia al ministerio de salud
## 2 Act_4_Ind_1 # de kits de SSR en la emergencia al ministerio de salud
## 3 Act_4_Ind_1 # de kits de SSR en la emergencia al ministerio de salud
## 4 Act_4_Ind_1 # de kits de SSR en la emergencia al ministerio de salud
## 5 ... ...
## response required type partnerName canton
## 1 13 FALSE quantity UNFPA HUAQUILLAS
## 2 <NA> FALSE quantity ACNUR SAN LORENZO
## 3 <NA> FALSE quantity ACNUR SANTO DOMINGO
## 4 <NA> FALSE quantity ACNUR TULCAN
## 5 ... ... ... ... ...
## province description formNameRecode
## 1 EL ORO <NA> Salud
## 2 ESMERALDAS <NA> Salud
## 3 SANTO DOMINGO DE LOS TSACHILAS <NA> Salud
## 4 CARCHI <NA> Salud
## 5 ... ... ...
Some description about the nature of data:
databaseId: the internal ActivityInfo id for databases
databaseName: the name of databases visible to users
folderId: the internal ActivityInfo id for folders
folderName: the name of folders visible to users
formId: the internal ActivityInfo id for forms
formName: the name of forms visible to users
subFormId: the internal ActivityInfo id for the sub-forms where the records are kept
subFormName: the name of the sub-forms visible to users
Month: indicating month when a record is entered
code: Schema question code
question: Question label indicated by the code
response: Response given by users
required: A boolean value to check whether the question is required to complete.
type: internal type for the code. The available types in the data are quantity, NARRATIVE, enumerated.
partnerName: The name of reporting partners. The name of implementing partners can be extracted from the data.
canton: The canton name of the record.
province: The province name of the record.
description: the description field further explaining what the question mean. The cells are represented as NA when fields not exists or not applicable.
Please see ActivityInfo documentation for more information about how the information is structured.
Before we begin, we shorten the form topic names by recoding them because they appear to be long and disarray the plots. The recode table below presents a lookup table for the form labels.
| formName | formNameRecode1 |
|---|---|
| Objectivo_1.1 | |
| Salud | Salud |
| Seguridad_alimentaria | AlimentSegur |
| Agua, saneamiento e higiene | Agua |
| Alojamiento Temporal | Alojamiento |
| Transporte humanitario | Transport |
| Necesidades básicas/Otro | Necesidades |
| Objectivo_1.2 | |
| Manejo de la información y entrega directa de la información a la población | Poblacion |
| Manejo de la información para socios y análisis de las necesidades | Socios |
| Objectivo_2.1 | |
| Protección general | General |
| Objectivo_2.2 | |
| Protección_Infancia | Infancia |
| Protección_VBG | VBG |
| Trata_y_tráfico | Trafico |
| Protection_Otro | Otro |
| Protección_LGBTI | LGBTI |
| Objectivo_3.1 | |
| Acceso_a_educación | Educacion |
| Acceso a vivienda y hábitat dignos en comunidades receptoras | Habitat |
| Objectivo_3.2 | |
| Medios de vida y formación técnico-profesional | Tecnico |
| Cohesión_social | SocialCohesion |
| Apoyo Educacional a Comunidades Receptoras | Educacional |
| Objectivo_4.1 | |
| Asistencia técnica para VBG-SSR | VBG_SSR |
| Asistencia técnica para protección/gestión de fronteras | Fronteras |
| Objectivo_4.2 | |
| Asistencia técnica para gestion de la informacion y coordinacion | Coordinacion |
| Objectivo_4.3 | |
| Asistencia técnica para el sector laboral | SectorLaboral |
| Asistencia técnica para protección | Proteccion |
| Asistencia técnica para protección de la infancia | ProteccionInfancia |
| Asistencia técnica para Salud | AsistenciaSalud |
| 1 Some short form names (e.g. 'Salud') stay as such, no need to make them even shorter | |
They are two types of partners in the database:
Reporting partners: Higher level of partners reporting directly in ActivityInfo.
Implementing partners: Partners reporting through a reporting partner.
The table below shows the count of reporting partner per each record:
ACNUR has nine hundred eighty-two records, which is 62.0% of the total records.
Second, NRC has one hundred thirty-eight records, which is 8.70% of the total records.
The most difference between percentages of the partners ACNUR and NRC is 53%.
| Reporting partner | Frequency | Relative frequency |
|---|---|---|
| ACNUR | 982 | 0.620 |
| NRC | 138 | 0.087 |
| PMA | 106 | 0.067 |
| UNICEF | 84 | 0.053 |
| OIM | 73 | 0.046 |
| UNFPA | 64 | 0.040 |
| CARE | 29 | 0.018 |
| Dialogo Diverso | 26 | 0.016 |
| Mision Scalabriniana | 18 | 0.011 |
| ADRA | 15 | 0.009 |
| RET | 15 | 0.009 |
| OPS/OMS | 8 | 0.005 |
| PNUD | 7 | 0.004 |
| JRS Ecuador | 5 | 0.003 |
| Plan Internacional | 5 | 0.003 |
| World Vision | 5 | 0.003 |
| UNESCO | 3 | 0.002 |
The table below shows the proportion of records entered by partners and sub-partners.
677 out of 982 total responses of ACNUR is actually coming from HIAS.
UNICEF has more diversed partners in terms of reporting. 44% of responses of UNICEF comes from HIAS. 25% of reporting comes from the UNICEF itself.
Under PMA, there are 13 sub-partners. HIAS reports 41% of these records.
Those are the total numbers of reporting in all database, the numbers are not specific to the narratives (multi-line text fields). In the next section, we count the number of reportings done only in the narrative sections.
| Implementing partner | Frequency | Relative frequency |
|---|---|---|
| ACNUR | ||
| HIAS | 677 | 0.689 |
| ACNUR | 291 | 0.296 |
| JRS Ecuador | 5 | 0.005 |
| NRC | 4 | 0.004 |
| ASA | 2 | 0.002 |
| Federación de Mujeres de Sucumbios | 2 | 0.002 |
| Federación de mujeres de Sucumbíos | 1 | 0.001 |
| NRC | ||
| NRC | 134 | 0.971 |
| ACNUR | 4 | 0.029 |
| OIM | ||
| OIM | 73 | 1.000 |
| UNFPA | ||
| UNFPA | 62 | 0.969 |
| RET | 2 | 0.031 |
| PMA | ||
| HIAS | 44 | 0.415 |
| ADRA | 10 | 0.094 |
| Buen Pastor | 5 | 0.047 |
| Fundación de Mujeres de Sucumbios | 5 | 0.047 |
| Fundación Tarabita | 5 | 0.047 |
| Hermanas Salesias | 5 | 0.047 |
| Hogar de Cristo | 5 | 0.047 |
| Pastoral Social Cáritas Tulcán | 5 | 0.047 |
| SJR | 5 | 0.047 |
| World Vision | 5 | 0.047 |
| Alas de Colibri | 4 | 0.038 |
| Casa Matilde | 4 | 0.038 |
| Patronato | 4 | 0.038 |
| UNICEF | ||
| HIAS | 37 | 0.440 |
| ADRA | 21 | 0.250 |
| UNICEF | 21 | 0.250 |
| NRC | 3 | 0.036 |
| Centro de Desarrollo y Autogestión | 2 | 0.024 |
| CARE | ||
| CARE | 29 | 1.000 |
| Dialogo Diverso | ||
| Dialogo Diverso | 25 | 0.962 |
| OIM | 1 | 0.038 |
| Mision Scalabriniana | ||
| Mision Scalabriniana | 18 | 1.000 |
| ADRA | ||
| ADRA | 15 | 1.000 |
| RET | ||
| RET | 15 | 1.000 |
| OPS/OMS | ||
| OPS/OMS | 8 | 1.000 |
| PNUD | ||
| PNUD | 7 | 1.000 |
| JRS Ecuador | ||
| JRS Ecuador | 5 | 1.000 |
| Plan Internacional | ||
| Plan Internacional | 5 | 1.000 |
| World Vision | ||
| World Vision | 5 | 1.000 |
| UNESCO | ||
| UNESCO | 3 | 1.000 |
Which reporting and implementing partners do report (in all fields)?
As per canton, province, partner etc.
| The number of direct and indirect fields per form topic | ||||
|---|---|---|---|---|
| ¿Implementacion directa o indirecta? | ||||
| formNameRecode | Directa | Indirecta | NAs | Total |
| Agua | 33 | 17 | 0 | 50 |
| Alojamiento | 39 | 24 | 0 | 63 |
| Coordinacion | 7 | 36 | 0 | 43 |
| Educacion | 22 | 72 | 0 | 94 |
| Educacional | 1 | 0 | 0 | 1 |
| Fronteras | 22 | 0 | 0 | 22 |
| General | 115 | 59 | 0 | 174 |
| Habitat | 21 | 13 | 0 | 34 |
| Infancia | 37 | 65 | 6 | 108 |
| Necesidades | 46 | 74 | 0 | 120 |
| Otro | 4 | 53 | 0 | 57 |
| Poblacion | 71 | 53 | 0 | 124 |
| Proteccion | 26 | 16 | 0 | 42 |
| Salud | 3 | 56 | 6 | 65 |
| SectorLaboral | 1 | 28 | 0 | 29 |
| SocialCohesion | 32 | 14 | 0 | 46 |
| Socios | 29 | 0 | 0 | 29 |
| Tecnico | 28 | 153 | 0 | 181 |
| Trafico | 4 | 0 | 0 | 4 |
| Transport | 5 | 0 | 0 | 5 |
| VBG | 55 | 72 | 0 | 127 |
| VBG_SSR | 17 | 2 | 0 | 19 |
| The number of direct and indirect fields per canton | ||||
|---|---|---|---|---|
| ¿Implementacion directa o indirecta? | ||||
| canton | Directa | Indirecta | NAs | Total |
| AMBATO | 3 | 0 | 0 | 3 |
| BAÑOS DE AGUA SANTA | 4 | 0 | 0 | 4 |
| CUENCA | 3 | 63 | 0 | 66 |
| ELOY ALFARO | 0 | 2 | 0 | 2 |
| ESMERALDAS | 31 | 58 | 0 | 89 |
| GUAYAQUIL | 30 | 88 | 0 | 118 |
| HUAQUILLAS | 69 | 41 | 2 | 112 |
| IBARRA | 69 | 79 | 1 | 149 |
| LAGO AGRIO | 92 | 111 | 1 | 204 |
| LATACUNGA | 4 | 0 | 0 | 4 |
| MACHALA | 8 | 8 | 1 | 17 |
| MANTA | 12 | 0 | 0 | 12 |
| ORELLANA | 1 | 0 | 0 | 1 |
| PEDERNALES | 1 | 0 | 0 | 1 |
| QUEVEDO | 4 | 0 | 0 | 4 |
| QUITO | 128 | 112 | 1 | 241 |
| RIOBAMBA | 3 | 0 | 0 | 3 |
| SALINAS | 1 | 0 | 0 | 1 |
| SAN LORENZO | 7 | 55 | 0 | 62 |
| SAN MIGUEL | 1 | 0 | 0 | 1 |
| SANTO DOMINGO | 2 | 68 | 0 | 70 |
| TULCAN | 145 | 122 | 6 | 273 |
| The number of direct and indirect fields per partners | ||||
|---|---|---|---|---|
| ¿Implementacion directa o indirecta? | ||||
| subPartnerName | Directa | Indirecta | NAs | Total |
| ACNUR | ||||
| ACNUR | 240 | 44 | 3 | 287 |
| ASA | 0 | 2 | 0 | 2 |
| Federación de Mujeres de Sucumbios | 0 | 2 | 0 | 2 |
| Federación de mujeres de Sucumbíos | 0 | 1 | 0 | 1 |
| HIAS | 0 | 664 | 0 | 664 |
| JRS Ecuador | 0 | 1 | 0 | 1 |
| NRC | 0 | 4 | 0 | 4 |
| ADRA | ||||
| ADRA | 13 | 0 | 2 | 15 |
| CARE | ||||
| CARE | 27 | 0 | 0 | 27 |
| Dialogo Diverso | ||||
| Dialogo Diverso | 13 | 0 | 0 | 13 |
| OIM | 0 | 1 | 0 | 1 |
| JRS Ecuador | ||||
| JRS Ecuador | 5 | 0 | 0 | 5 |
| Mision Scalabriniana | ||||
| Mision Scalabriniana | 18 | 0 | 0 | 18 |
| NRC | ||||
| ACNUR | 0 | 4 | 0 | 4 |
| NRC | 133 | 0 | 1 | 134 |
| OIM | ||||
| OIM | 73 | 0 | 0 | 73 |
| OPS/OMS | ||||
| OPS/OMS | 1 | 4 | 1 | 6 |
| Plan Internacional | ||||
| Plan Internacional | 3 | 0 | 0 | 3 |
| PNUD | ||||
| PNUD | 7 | 0 | 0 | 7 |
| RET | ||||
| RET | 15 | 0 | 0 | 15 |
| UNESCO | ||||
| UNESCO | 3 | 0 | 0 | 3 |
| UNFPA | ||||
| RET | 0 | 2 | 0 | 2 |
| UNFPA | 58 | 0 | 4 | 62 |
| UNICEF | ||||
| ADRA | 0 | 21 | 0 | 21 |
| Centro de Desarrollo y Autogestión | 0 | 2 | 0 | 2 |
| HIAS | 0 | 37 | 0 | 37 |
| NRC | 0 | 3 | 0 | 3 |
| UNICEF | 4 | 15 | 1 | 20 |
| World Vision | ||||
| World Vision | 5 | 0 | 0 | 5 |
In this section, we focus on a subset of the reports, which do particularly have the multi-text fields, called “Narrative data” in ActivityInfo terms. Plain saying that narrative data is multi-line text fields allowing users to enter long texts.
Note that we also keep the narratives that are empty (which are displayed as NA, Not Available).
| formNameRecode | Response | Response rate1 | ||
|---|---|---|---|---|
| filled | missing | total | ||
| Tecnico | 182 | 1085 | 1267 | 0.168 |
| Poblacion | 123 | 249 | 372 | 0.494 |
| VBG | 78 | 430 | 508 | 0.181 |
| Coordinacion | 43 | 215 | 258 | 0.200 |
| Educacion | 43 | 333 | 376 | 0.129 |
| SectorLaboral | 29 | 116 | 145 | 0.250 |
| SocialCohesion | 28 | 110 | 138 | 0.255 |
| Socios | 28 | 175 | 203 | 0.160 |
| Proteccion | 25 | 59 | 84 | 0.424 |
| Fronteras | 22 | 44 | 66 | 0.500 |
| Habitat | 18 | 84 | 102 | 0.214 |
| Alojamiento | 12 | 51 | 63 | 0.235 |
| Necesidades | 12 | 228 | 240 | 0.053 |
| VBG_SSR | 11 | 27 | 38 | 0.407 |
| LGBTI | 7 | 17 | 24 | 0.412 |
| Salud | 6 | 124 | 130 | 0.048 |
| Trafico | 4 | 8 | 12 | 0.500 |
| Agua | 1 | 99 | 100 | 0.010 |
| Educacional | 1 | 1 | 2 | 1.000 |
| ProteccionInfancia | 1 | 0 | 1 | Inf |
| AlimentSegur | 0 | 118 | 118 | 0.000 |
1 Divide the number of filled responses in the number of missing responses |
||||
The table shows the narrative fields with count of ’full’ and ‘empty’ fields.
The form Tecnico (Medios de vida y formación técnico-profesional) has 182 and 1085 records in a total of 1267 records.
The form VBG (Protección_VBG) has 78 and 430 records in a total of 508 records.
The form Educacion (Acceso_a_educación) has 43 and 333 records in a total of 376 records.
The form Poblacion (Manejo de la información y entrega directa de la información a la población) has 123 and 249 records in a total of 372 records.
The form Coordinacion (Asistencia técnica para gestion de la informacion y coordinacion) has 43 and 215 records in a total of 258 records.
Please note that the following form topics are not included in the table above because they do not contain any narrative fields: Transporte humanitario, Protección general, Protección_Infancia, Protection_Otro, Asistencia técnica para Salud
Here, we look at the partners entering narrative data. The number of missing records (namely NAs) are excluded.
The number of ‘Reporting Partners’ and ‘Implementing Partners’ reporting narrative (multi-line text) data:
As we have seen previously, Not all Reporting and Implementing Partners record multi-line narrative textual data. For instance, the partner PMA has lots of Implementing Partners reporting for the different data types (as seen in above) but there are no narratives from them.
71% of the narrative records are entered by the implementing partner HIAS reported via ACNUR. Only 27% of the narrative records are entered by ACNUR itself.
Also HIAS enters 26% of the narrative records via UNICEF.
The rest of the “reporting partners” do not have any “implementing partners”, as it seems that they do the implementation: CARE, Dialogo Diverso, JRS Ecuador, Mision Scalabriniana, OIM, OPS/OMS, Plan Internacional, PNUD, UNESCO, UNFPA.
| Canton and provinces | |||
|---|---|---|---|
| The number of reports in the multi-text (narrative) fields | |||
| Canton | Frequency | Relative frequency (canton) | Relative frequency (province) |
| PICHINCHA | |||
| QUITO | 756 | 1.000 | 0.178 |
| CARCHI | |||
| TULCAN | 689 | 1.000 | 0.162 |
| SUCUMBIOS | |||
| LAGO AGRIO | 617 | 1.000 | 0.145 |
| IMBABURA | |||
| IBARRA | 463 | 1.000 | 0.109 |
| GUAYAS | |||
| GUAYAQUIL | 375 | 1.000 | 0.088 |
| ESMERALDAS | |||
| ESMERALDAS | 269 | 0.568 | 0.063 |
| SAN LORENZO | 197 | 0.416 | 0.046 |
| ELOY ALFARO | 8 | 0.017 | 0.002 |
| SANTO DOMINGO DE LOS TSACHILAS | |||
| SANTO DOMINGO | 265 | 1.000 | 0.062 |
| EL ORO | |||
| HUAQUILLAS | 261 | 0.900 | 0.061 |
| MACHALA | 29 | 0.100 | 0.007 |
| AZUAY | |||
| CUENCA | 235 | 1.000 | 0.055 |
| MANABI | |||
| MANTA | 30 | 1.000 | 0.007 |
| COTOPAXI | |||
| LATACUNGA | 11 | 1.000 | 0.003 |
| TUNGURAHUA | |||
| BAÑOS DE AGUA SANTA | 11 | 0.611 | 0.003 |
| AMBATO | 7 | 0.389 | 0.002 |
| LOS RIOS | |||
| QUEVEDO | 10 | 1.000 | 0.002 |
| BOLIVAR | |||
| SAN MIGUEL | 7 | 1.000 | 0.002 |
| CHIMBORAZO | |||
| RIOBAMBA | 7 | 1.000 | 0.002 |
Treemap plot showing canton and province reporting frequencies.
Response quality is a term that means how much response the questions receive. The idea is to find cases that affect the response quality to understand if they work or not under some conditions. In the end, the results may reveal some handful insights about the quality of textual responses in the narrative fields.
Some questions to research response quality by measuring word count:
Is there any relationship between the word counts of response, question and description fields?
What is the distribution between response word count and explanatory variables such as the question, form topic, canton name, partner name, etc.
Assumptions:
In other words, we assume that the more word the better is. The limitations are based on the unequal distribution of the data. The word count of responses and questions can be related to other things, such as the questions require short answers so then the responses tend to be shorter.
Additionally, we can have a cross-analysis to test these outcomes. It might be a good idea to have a small subset of data and ask an expert to test the assumptions qualitatively. For instance, we can take the first twenty responses with the highest word count and the last twenty responses with the lowest word count. We chose the extreme directions because they point out the greatest differences which are easier to test assumptions.
One issue with the nature of the questions is that they are only unique in a form. These questions can be distributed across multiple forms. The questions sharing the same name will have different meanings. For instance, the question “Cualitativo” from the form “Salud” should imply different thing than the question “Cualitativo” from the form “Protección_VBG”.
In order to solve this kind of problem:
We can combine question with the form and also its folder label. There we can achieve a unique name for each question.
Another thing to resolve this would be doing analysis to move the analysis up to form level. In this file, we did both, therefore the analysis shown as below:
Count of responses per topic/question (note that missing entries NAs are removed):
| folderName | formName | formNameRecode | Month | question | response | description | partnerName | subPartnerName | province | canton | .responseWordCount | .questionWordCount | .descriptionWordCount |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Objectivo_1.1 | Salud | Salud | 2019-02 | Cualitativo | 1. Entrega de k | Descripción de | UNFPA | UNFPA | CARCHI | TULCAN | 302 | 1 | 17 |
| Objectivo_1.1 | Salud | Salud | 2019-02 | Cualitativo | 1. Entrega de k | Descripción de | UNFPA | UNFPA | EL ORO | HUAQUILLAS | 302 | 1 | 17 |
| Objectivo_1.1 | Salud | Salud | 2019-02 | Cualitativo | 1. Entrega de k | Descripción de | UNFPA | UNFPA | EL ORO | MACHALA | 302 | 1 | 17 |
| Objectivo_1.1 | Salud | Salud | 2019-02 | Cualitativo | 1. Entrega de k | Descripción de | UNFPA | UNFPA | SUCUMBIOS | LAGO AGRIO | 302 | 1 | 17 |
| Objectivo_1.1 | Salud | Salud | 2019-04 | Cualitativo | Se complementa | Descripción de | UNFPA | UNFPA | SUCUMBIOS | LAGO AGRIO | 13 | 1 | 17 |
| Objectivo_1.1 | Salud | Salud | 2019-04 | Cualitativo | 233 Equipos méd | Descripción de | UNFPA | UNFPA | ESMERALDAS | SAN LORENZO | 46 | 1 | 17 |
It’s also a good practice to see the number of questions. For example, one question has two responses, therefore they’re short.
Box plots are used to visualize the measure of spread showing the variability and dispersion of the data.
In the boxplot above, we see a number of things:
each individual black point in a group represents a “response” in the records, and it position indicates the value of word count;
left and right borders of the central rectangle (colored red) presents the first and third quartile values of IQR respectively;
the line in the middle of the rectangle indicates the median value;
the end of the lines streched from both right and left sides of the central rectangle point the maximum and minimum values;
the orange colored points show the outliers.
Some insights from this plot can be: TODO
The standard deviation is a single number statistics to show the measure of spread in data.
| Deviation of response word counts per form topic | |
|---|---|
| Measure the spread with standard deviation | |
| formNameRecode | SD |
| Agua | NA |
| Alojamiento | 71.15 |
| Coordinacion | 59.13 |
| Educacion | 56.56 |
| Educacional | NA |
| Fronteras | 45.15 |
| Habitat | 6.64 |
| LGBTI | 123.48 |
| Necesidades | 19.37 |
| Poblacion | 69.82 |
| Proteccion | 21.69 |
| ProteccionInfancia | NA |
| Salud | 141.10 |
| SectorLaboral | 21.80 |
| SocialCohesion | 29.35 |
| Socios | 106.79 |
| Tecnico | 49.04 |
| Trafico | 17.30 |
| VBG | 44.27 |
| VBG_SSR | 34.22 |
In the plot above, the box plot of form topics and response word counts based on the raw data, the outliers are shown in orange color. Outliers are the points placed outside the whiskers, which is the long line, of the boxplot.
The response word count distribution per form topic categorized by partner name:
The response word count distribution per form topic categorized by canton name:
A caveat: Reducing multiple values down to a single value should be avoided in the early stages of the analysis because reducing hides a lot e.g. a bar chart showing average the word count per partner. Some partners may write longer than others, because:
They actually write longer than other partners.
The questions they answered require short answers.
Some questions have the description field giving extra details about the questions.
Do some questions with the extra description field have better response quality than the questions which do not have it?
Looking at the table containing form name, question, description and so on:
We see in the plot below that the response word counts per form and colored if a response has a description field or not. Having a description field or not is calculated as that a description field has a minimum one word.
The responses with the longest word counts are the ones with description. Nevertheless, it is not so easy to see a clear trend that there’s a correlation between response word count and description fields. Interestingly, the form topic Protección_VBG has no description fields at all in its form topics.
TODO ANOVA
The correlation between the word count of different fields in the ActivityInfo:
| .responseWordCount | .questionWordCount | .descriptionWordCount | |
|---|---|---|---|
| .responseWordCount | 1.00 | -0.14 | 0.13 |
| .questionWordCount | -0.14 | 1.00 | -0.59 |
| .descriptionWordCount | 0.13 | -0.59 | 1.00 |
1 words. And those words were just not important (as they are TEST).TODO
We can look at multiple continuous variables in our data.
word count of response field: response.wc
word count of question field: question.wc
word count of description field: description.wc
Given this data, the model formula showing dependent and independent variables can be as follows:
$ response.wc question.wc $
$ response.wc question.wc + description.wc $
In that sense the word count of response field is the dependent variable and word count of question and word count of description fields are the independent variables in the regression.
Thus, we expect to have more word count in question and description fields to have a positive effect on the word count of response field.
Scatter plots help understand the characteristics of those variables. However, we miss a general understanding that is the trend line.
The gray area around the lines shows the confidence band at the 0.95 level. Although there’s a straight slope in the linear regression line, we cannot say that the trend line is robust because the confidence band representing the uncertainty in the estimate is wide.
TODO In fact, it is called the binomial logistic regression. When one of the independent variables is dichotomous (having two categories), ...
We do the hypothesis testing based on the assumptions we have.
$ H_0 $ : More word count in questions results in more word count in responses.
TODO
In that section, we take text as data.
Describe how to prepare textual data and what common steps are usually performed.
They are usually four steps involved in this process:
1. Tokenization
Tokenization means to split a text into tokens considered meaningful units of text. A token can either be a word (and often it is) or a group of words (such as bigram), or even a sentence that depends on the level of analysis.
Perform stemming, which you bring words (nouns/verbs) back to base or infinitive forms, will be the next step after tokenization, so we can get the essence of words.
2. Strip punctuation
Punctuation is often not required in text analysis (unless a researcher wants to tokenize the text based on a specific classifier such as sentence tokens); therefore, they create noise.
3. Convert text into lowercase
When the text turned into lowercase, for instance, the words respuesta and Respuesta will no longer be taken as different words.
4. Exclude stopwords & numbers
Stop words usually mean the most common words in a language that will bring no significant results in analysis. They are overly distributed in the text and they will not give so meaningful results itself. Stop-words are including articles (el/la), conjunctions (y), pronouns (yo/tú/etc.) and so on.
In text mining, this process is usually done after the text converted into lowercase so one does not have to provide stop words including both lower and sentence case versions.
We have imported a list of Spanish stopwords data (source here, and that’s the alternative for stopwords_es list from the corpus package) and perform a filtering join returning tokens from textual data by excluding the words listed in the stopwords. that only returns the tokens not listed in the stopwords.
The original tokens for the response originally have 38216 rows. However, after merging stop words, the number of rows have decreased to 19429 and that the change in between is 51%.
It’s also possible to add more custom words e.g. ACNUR, if some organization names are not desired, or violencia, if some words are overused and brings no further explanation, in the results.
5. Perform stemming
Stemming is a process that removes the suffixes (and sometimes prefixes) of the words and bring them to the base form. We use “Hunspell” stemmer from the package hunspell that provides more precise stemming behavior.
From that point onwards, we will use stemmed words instead of the raw tokenized words because stemmed words give us better information.
After stemming, the words look like this:
| word | word_stem |
|---|---|
| entrega | entregar |
| kits | kits |
| salud | salud |
| sexual | sexual |
| reproductiva | productivo |
| ... | ... |
Sentiment analysis (also called as opinion mining) is a technique to understand the emotional meanings of text given by a dictionary describing the positive/negative words that already done by humans.
The responses seem to be written with a formal tone of voice; therefore, the responses may not show any sentiment at all.
First, we find a sentiment lexicon for the Spanish language (source here).
A wordcloud showing positive and negative words in the responses:
TODO
We can search the multi-type terms in the responses.
So the end result looks like this:
| word1 | word2 | bigram |
|---|---|---|
| baterias | sanitarias | baterias sanitario |
| camas | literas | cama litera |
| plaza | metálicas | plaza metálico |
| metálicas | colchones | metálico colchón |
| colchones | 34 | colchón 34 |
| ... | ... | ... |
tf-idf values can also be calculated for bigrams, and visualized within each reporting/implementing partner, province/canton and so forth.
TODO
TODO
Silge J, Robinson D (2017). Text mining with R: A tidy approach. O’Reilly Media, Inc.
Stevenson, M. (2019, Jan 26). Text Analysis with tidytext. Retrieved July 18, 2019, from https://markrstevenson.com/blog/wXMUdQCIfHqwTzrnProy